Results 141 to 150 of about 258,537 (280)

A comprehensive dataset of customer behavior in Latin American Fintech: 12-month transactional and demographic data for churn analysis. [PDF]

Data Brief
Muñoz-Guerrero LE, Ceballos YF, Trejos-Rojas LD.   +2 more
europepmc   +1 more source

QS4D: Quantization‐Aware Training for Efficient Hardware Deployment of Structured State‐Space Sequential Models

Advanced Intelligent Systems, EarlyView.
Quantization‐aware training creates resource‐efficient structured state space sequential S4(D) models for ultra‐long sequence processing in edge AI hardware. Including quantization during training leads to efficiency gains compared to pure post‐training quantization.
Sebastian Siegel, Ming‐Jay Yang, Younes Bouhadjar, Maxime Fabre, Emre Neftci, John Paul Strachan   +5 more
wiley   +1 more source

Speciation, Structural Refinement, and Distribution of Ti Sites in Titanium Silicalite‐1 From 47/49Ti NMR Crystallography at 28.2 Tesla

Angewandte Chemie, EarlyView.
Titanium silicalite‐1 (TS‐1), used in industrial selective oxidation processes, depends on specific, yet still unresolved Ti sites, dispersed within the framework of an MFI‐zeolite. Applying high field (28.2 T) 47/49Ti NMR and 17O NMR spectroscopy for an array of TS‐1 catalysts enables the development of an NMR crystallography protocol and the ...
Christoph J. Kaul, Jonas Koppe, Lukas Lätsch, Michael Wörle, Sadig Aghazada, Jacob B. Holmes, Christina Wartmann, Mingji Zheng, Albrecht Berkessel, Trees De Baerdemaeker, Andrei‐Nicolae Parvulescu, Karsten Seidel, J. Henrique Teles, Alexander V. Yakimov, Christophe Copéret   +14 more
wiley   +2 more sources

Some New Maximally Chaotic Discrete Maps. [PDF]

Entropy (Basel)
Choi H, Kim G, Song HY, Shin S, Lee C, Noh H.   +5 more
europepmc   +1 more source

Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs

Advanced Intelligent Systems, EarlyView.
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza, Bruno José Olivieri de Souza, Markus Endler   +2 more
wiley   +1 more source

Hardware acceleration of number theoretic transform for zk‐SNARK

Engineering Reports, EarlyView., 2023
An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao, Dong Ding, Feng Wang, Pengcheng Hua, Ning Wang, Qin Wu, Zhilei Chai   +6 more
wiley   +1 more source

Consumer mobility and shopping mall visitation data from smartphone tracking in Johannesburg, South Africa. [PDF]

Data Brief
Cheruiyot K, Karuitha J, Adewunmi Y, Simbanegavi P, Moenyane K, Cohen K.   +5 more
europepmc   +1 more source

ControlIt: A Universal Framework for Translational, Adaptive, and Online Brain–Computer Interfaces

Advanced Intelligent Systems, EarlyView.
Brain–computer interfaces (BCIs) lack a unified platform that works across signals and algorithms. ControlIt, an open‐source, modular ROS2‐based BCI framework supporting electroencephalography (EEG), electrocorticography (ECoG), and spike‐based decoding across both classification and regression tasks is presented.
Wanlin Yang, Chenyang Li, Xinxiu Xu, Xindong Wang, Yongcheng Guo, Xiaoke Chai, Tianqin Cao, Jiuxiang Song, Nan Wang, Xueming Zhang, Yi Yang, Cong Zheng, He Cui   +12 more
wiley   +1 more source

Shifted Poisson structures on higher Chevalley-Eilenberg algebras. [PDF]

Lett Math Phys
Kemp C, Laugwitz R, Schenkel A.
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source